tune_cpdbee_3D function

Tunes 3D event detection using labeled data

This function finds best parameters for 3D event detection using labeled data.

tune_cpdbee_3D(
  x,
  cl,
  alpha_min = 0.95,
  alpha_max = 0.98,
  alpha_step = 0.01,
  epsilon_min = 2,
  epsilon_max = 12,
  epsilon_step = 2,
  minPts_min = 8,
  minPts_max = 16,
  minPts_step = 2
)

Arguments

• x: The data in an mxn matrix or dataframe.
• cl: The actual locations of the events.
• alpha_min: The minimum threshold value.
• alpha_max: The maximum threshold value.
• alpha_step: The incremental step size for alpha.
• epsilon_min: The minimum epsilon value for DBSCAN clustering.
• epsilon_max: The maximum epsilon value for DBSCAN clustering.
• epsilon_step: The incremental step size for epsilon for DBSCAN clustering.
• minPts_min: The minimum minPts value for for DBSCAN clustering.
• minPts_max: The maximum minPts value for for DBSCAN clustering.
• minPts_step: The incremental step size for minPts for DBSCAN clustering.

Returns

A list with following components - best: The best threshold, epsilon and MinPts for 2D event detection and the associated Jaccard Index.

• all: All parameter values used and the associated Jaccard Index values.

Examples

## Not run:

set.seed(1)
arr <- array(rnorm(12000),dim=c(40,25,30))
arr[25:33,12:20, 20:23] <- 10
# Getting events
out <- get_clusters_3d(arr, thres=0.985) 
out <- tune_cpdbee_3D(arr, out$data[ ,1:3])
out$best
## End(Not run)